package com.demo.spark.stream;


import java.util.Arrays;
import java.util.List;
import java.util.regex.Pattern;

import scala.Tuple2;

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.function.*;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.Optional;
import org.apache.spark.api.java.StorageLevels;
import org.apache.spark.streaming.Durations;
import org.apache.spark.streaming.State;
import org.apache.spark.streaming.StateSpec;
import org.apache.spark.streaming.api.java.*;

/**
 * Counts words cumulatively in UTF8 encoded, '\n' delimited text received from the network every
 * second starting with initial value of word count.
 * Usage: JavaStatefulNetworkWordCount <hostname> <port>
 * <hostname> and <port> describe the TCP server that Spark Streaming would connect to receive
 * data.
 * <p>
 * To run this on your local machine, you need to first run a Netcat server
 * `$ nc -lk 9999`
 * and then run the example
 * `$ bin/run-example
 * org.apache.spark.examples.streaming.JavaStatefulNetworkWordCount localhost 9999`
 */
public class StreamStatfulWordCount {
    private static final Pattern SPACE = Pattern.compile(" ");

    public static void main(String[] args) throws Exception {
        // Create the context with a 1 second batch size
        SparkConf sparkConf = new SparkConf().setAppName("JavaStatefulNetworkWordCount").setMaster("local[*]");

        JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, Durations.seconds(3));
        ssc.sparkContext().setLogLevel("ERROR");
        ssc.checkpoint(".");

        // Initial state RDD input to mapWithState
        @SuppressWarnings("unchecked")
        List<Tuple2<String, Integer>> tuples =
                Arrays.asList(new Tuple2<>("hello", 1), new Tuple2<>("world", 1));
        JavaPairRDD<String, Integer> initialRDD = ssc.sparkContext().parallelizePairs(tuples);

        JavaReceiverInputDStream<String> lines = ssc.socketTextStream(
                "localhost", 9999, StorageLevels.MEMORY_AND_DISK_SER_2);

        JavaDStream<String> words = lines.flatMap(x -> Arrays.asList(SPACE.split(x)).iterator());

        JavaPairDStream<String, Integer> wordsDstream = words.mapToPair(s -> new Tuple2<>(s, 1));

        // Update the cumulative count function
        Function3<String, Optional<Integer>, State<Integer>, Tuple2<String, Integer>> mappingFunc =new Function3<String, Optional<Integer>, State<Integer>, Tuple2<String, Integer>>() {
            @Override
            public Tuple2<String, Integer> call(String word, Optional<Integer> one, State<Integer> state) throws Exception {
                int sum = one.orElse(0) + (state.exists() ? state.get() : 0);
                Tuple2<String, Integer> output = new Tuple2<>(word, sum);
                state.update(sum);
                return output;
            }
        };

        // DStream made of get cumulative counts that get updated in every batch
        JavaMapWithStateDStream<String, Integer, Integer, Tuple2<String, Integer>> stateDstream =
                wordsDstream.mapWithState(StateSpec.function(mappingFunc).initialState(initialRDD));

        stateDstream.print();
        ssc.start();
        ssc.awaitTermination();
    }
}